



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase:   Union[str, Any]					=		transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def  lowerCamelCase__			(		_A							):
  if isinstance(_A					,     torch.Tensor							):
    return image
  elif isinstance(_A					,     PIL.Image.Image							):
    a		:   List[str]	  =	[image]

  a		:   List[str]	  =	[trans(img.convert('RGB'							)							) for img in image]
  a		:   List[str]	  =	torch.stack(_A							)
  return image

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    List[str]		,     __snake_case					:    Dict		,     __snake_case					:    Any      ):
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      a		:   Optional[int]	  =	DDIMScheduler.from_config(scheduler.config      )

      self.register_modules(unet=__snake_case		,     scheduler=__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      if strength < 0 or strength > 1:
        raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    Optional[int]		,     __snake_case					:    Tuple		,     __snake_case					:    int      ):
      # get the original timestep using init_timestep
      a		:   Union[str, Any]	  =	min(int(num_inference_steps * strength      )		,     __snake_case      )

      a		:   Any	  =	max(num_inference_steps - init_timestep		,     0      )
      a		:   Any	  =	self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start







    def      lowercase_			( self					:    Any		,     __snake_case					:    Optional[int]		,     __snake_case					:    Dict		,     __snake_case					:    Dict		,     __snake_case					:    Tuple		,     __snake_case					:    List[Any]		,     __snake_case					:    Any=None      ):
      if not isinstance(__snake_case		,     (torch.Tensor, PIL.Image.Image, list)      ):
        raise ValueError(
            F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case      )}"""      )

      a		:   List[Any]	  =	image.to(device=__snake_case		,     dtype=__snake_case      )

      if isinstance(__snake_case		,     __snake_case      ) and len(__snake_case      ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(__snake_case      )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

      a		:   Optional[int]	  =	init_latents.shape
      a		:   Dict	  =	randn_tensor(__snake_case		,     generator=__snake_case		,     device=__snake_case		,     dtype=__snake_case      )

      # get latents
      print('add noise to latents at timestep'		,     __snake_case      )
      a		:   int	  =	self.scheduler.add_noise(__snake_case		,     __snake_case		,     __snake_case      )
      a		:   List[Any]	  =	init_latents

      return latents







    @torch.no_grad()
    def __call__( self					:    Optional[Any]		,     __snake_case					:    Union[torch.FloatTensor, PIL.Image.Image] = None		,     __snake_case					:    float = 0.8		,     __snake_case					:    int = 1		,     __snake_case					:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __snake_case					:    float = 0.0		,     __snake_case					:    int = 50		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[str] = "pil"		,     __snake_case					:    bool = True		,     ):
      self.check_inputs(__snake_case      )

      # 2. Preprocess image
      a		:   Optional[Any]	  =	preprocess(__snake_case      )

      # 3. set timesteps
      self.scheduler.set_timesteps(__snake_case		,     device=self.device      )
      a  ,      a		:   Optional[Any]	  =	self.get_timesteps(__snake_case		,     __snake_case		,     self.device      )
      a		:   Union[str, Any]	  =	timesteps[:1].repeat(__snake_case      )

      # 4. Prepare latent variables
      a		:   List[Any]	  =	self.prepare_latents(__snake_case		,     __snake_case		,     __snake_case		,     self.unet.dtype		,     self.device		,     __snake_case      )
      a		:   Dict	  =	latents

      # 5. Denoising loop
      for t in self.progress_bar(__snake_case      ):
        # 1. predict noise model_output
        a		:   Optional[Any]	  =	self.unet(__snake_case		,     __snake_case      ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        a		:   Optional[Any]	  =	self.scheduler.step(
            __snake_case		,     __snake_case		,     __snake_case		,     eta=__snake_case		,     use_clipped_model_output=__snake_case		,     generator=__snake_case		,     ).prev_sample

      a		:   List[str]	  =	(image / 2 + 0.5).clamp(0		,     1      )
      a		:   List[Any]	  =	image.cpu().permute(0		,     2		,     3		,     1      ).numpy()
      if output_type == "pil":
        a		:   List[str]	  =	self.numpy_to_pil(__snake_case      )

      if not return_dict:
        return (image, latent_timestep.item())

      return ImagePipelineOutput(images=__snake_case      )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )